Project Data Analysis by BENJAMIN_DK_LUONG
This data set contains 1,599 red wines with 12 variables on the chemical properties of the wine. These variables are fixed.acidity, volatile.acidity, citric.acid, residual.sugar, chlorides, free.sulfur.dioxide, total.sulfur.dioxide, density, pH, sulphates, alcohol, and quality. At least 3 wine experts rated the quality of each wine, providing a rating between 0 (very bad) and 10 (very excellent).
Input variables (based on physicochemical tests): 1 - fixed acidity (tartaric acid - g / dm^3) 2 - volatile acidity (acetic acid - g / dm^3) 3 - citric acid (g / dm^3) 4 - residual sugar (g / dm^3) 5 - chlorides (sodium chloride - g / dm^3 6 - free sulfur dioxide (mg / dm^3) 7 - total sulfur dioxide (mg / dm^3) 8 - density (g / cm^3) 9 - pH 10 - sulphates (potassium sulphate - g / dm3) 11 - alcohol (% by volume) Output variable (based on sensory data): 12 - quality (score between 0 and 10)
Description of attributes:
1 - fixed acidity: most acids involved with wine or fixed or nonvolatile (do not evaporate readily)
2 - volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste
3 - citric acid: found in small quantities, citric acid can add ‘freshness’ and flavor to wines
4 - residual sugar: the amount of sugar remaining after fermentation stops, it’s rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet
5 - chlorides: the amount of salt in the wine
6 - free sulfur dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine
7 - total sulfur dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine
8 - density: the density of water is close to that of water depending on the percent alcohol and sugar content
9 - pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale
10 - sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, which acts as an antimicrobial and antioxidant
11 - alcohol: the percent alcohol content of the wine
Output variable (based on sensory data): 12 - quality (score between 0 and 10)
Sources: Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009
Univariate Plots Section
## X fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 1 7.4 0.70 0.00 1.9 0.076
## 2 2 7.8 0.88 0.00 2.6 0.098
## 3 3 7.8 0.76 0.04 2.3 0.092
## 4 4 11.2 0.28 0.56 1.9 0.075
## 5 5 7.4 0.70 0.00 1.9 0.076
## 6 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5This data contains 1599 rows.
## [1] 1599 13## 'data.frame': 1599 obs. of 13 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...## X fixed.acidity volatile.acidity citric.acid
## Min. : 1.0 Min. : 4.60 Min. :0.1200 Min. :0.000
## 1st Qu.: 400.5 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090
## Median : 800.0 Median : 7.90 Median :0.5200 Median :0.260
## Mean : 800.0 Mean : 8.32 Mean :0.5278 Mean :0.271
## 3rd Qu.:1199.5 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420
## Max. :1599.0 Max. :15.90 Max. :1.5800 Max. :1.000
## residual.sugar chlorides free.sulfur.dioxide
## Min. : 0.900 Min. :0.01200 Min. : 1.00
## 1st Qu.: 1.900 1st Qu.:0.07000 1st Qu.: 7.00
## Median : 2.200 Median :0.07900 Median :14.00
## Mean : 2.539 Mean :0.08747 Mean :15.87
## 3rd Qu.: 2.600 3rd Qu.:0.09000 3rd Qu.:21.00
## Max. :15.500 Max. :0.61100 Max. :72.00
## total.sulfur.dioxide density pH sulphates
## Min. : 6.00 Min. :0.9901 Min. :2.740 Min. :0.3300
## 1st Qu.: 22.00 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500
## Median : 38.00 Median :0.9968 Median :3.310 Median :0.6200
## Mean : 46.47 Mean :0.9967 Mean :3.311 Mean :0.6581
## 3rd Qu.: 62.00 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300
## Max. :289.00 Max. :1.0037 Max. :4.010 Max. :2.0000
## alcohol quality
## Min. : 8.40 Min. :3.000
## 1st Qu.: 9.50 1st Qu.:5.000
## Median :10.20 Median :6.000
## Mean :10.42 Mean :5.636
## 3rd Qu.:11.10 3rd Qu.:6.000
## Max. :14.90 Max. :8.000Quality of wine
summary(data$quality)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.636 6.000 8.000table(data$quality)##
## 3 4 5 6 7 8
## 10 53 681 638 199 18We can see that most of the quality rating are 5 or 6. Median is 6, and mean is 5.636.
Now we need to change the quality into categorial variable, so we can plot them by group.
data$quality <- as.character(data$quality)str(data)## 'data.frame': 1599 obs. of 13 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : chr "5" "5" "5" "6" ...## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Ignoring unknown parameters: binwidth, bins, pad
## Warning: Ignoring unknown parameters: binwidth, bins, pad## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
After looking at the graph above, I see that there are some skewed graphs such as redidual.sugar, chlorieds, free.sulfur.dioxide, total.sulfur.dioxide, sulphates, and alcohol. I want to see how the graphs change when I plot them using a log scale. Please take a look at the graph below to see the change.
## Warning: Ignoring unknown parameters: binwidth, bins, pad## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Univariate Analysis
What is the structure of your dataset?
This data set contains 1,599 red wines with 12 variables on the chemical properties of the wine. These variables are fixed.acidity, volatile.acidity, citric.acid, residual.sugar, chlorides, free.sulfur.dioxide, total.sulfur.dioxide, density, pH, sulphates, alcohol, and quality.
All variables are numbers and integer. There is no categorial variable. However, we can turn quality into categorial variable. We can analyze all other variables to find the relationship to the quality.
What is/are the main feature(s) of interest in your dataset?
I am interested in the quality of red wine. I need to determine which chemical properties influence the quality of red wines. I believe there are some chemical properties can influence the quality of red wines.
What other features in the dataset do you think will help support your investigation into your feature(s) of interest?
Volatile.acidity, sulphates, and alcohol are probably important variables related to the change of quality.
Did you create any new variables from existing variables in the dataset?
No, I did not create any new variables. However, I changed the data type of quality variable from integer to character.
Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?
I see that there are some skewed graphs such as redidual.sugar, chlorieds, free.sulfur.dioxide, total.sulfur.dioxide, sulphates, and alcohol. That’s why I log-transformed the skewed graphs. It’s easier for me to see the data after I log-transformed.
Bivariate Plots Section
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
I checked the correlation between all the variable in the dataset. I would like to plot some graphs to see the correlation in pairs:
- citric.acid vs fixed.acidity : 0.672
- density vs fixed.acidity : 0.66
- density vs citric.acid 0.36
- citric.acid vs volatile.acidity -0.552
- pH vs fixed.acidity -0.68
- pH vs citric.acid -0.54
- pH vs density -0.34
- sulphates vs citric.acid 0.31
- sulphates vs chlorides 0.37
- alcohol vs density -0.496
- quality vs alcohol 0.47
citric.acid vs fixed.acidity 0.672
density vs fixed.acidity 0.66
density vs citric.acid 0.36
citric.acid vs volatile.acidity -0.552
pH vs fixed.acidity -0.68
pH vs citric.acid -0.54
pH vs density -0.34
sulphates vs citric.acid 0.31
sulphates vs chlorides 0.37
alcohol vs density -0.496
quality vs alcohol
quality vs citric.acid
quality vs fixed.acidity
quality vs volatile.acidity
quality vs density
quality vs pH
quality vs sulphates
quality vs alcohol 0.47
Bivariate Analysis
Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?
-By looking at the boxplots above, when the quality get higher: sulphates,alcohol tend to get higher and volatile.acidity tend to get lower
Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?
-By looking at the boxplots above, when the quality get higher: citric.acid tends to get higher and density, pH tend to get lower
What was the strongest relationship you found?
Positive correlation: - citric.acid vs fixed.acidity : 0.672 . When citric.acid increases, fixed.acidity increase. - density vs fixed.acidity : 0.66 . When density increases, fixed.acidity increase.
Negative correlation: - pH vs fixed.acidity -0.68 . When pH increases, fixed.acidity decrease.
Multivariate Plots Section
citric.acid vs fixed.acidity 0.672
density vs fixed.acidity 0.66
pH vs fixed.acidity -0.68
pH vs citric.acid -0.54
pH vs density -0.34
sulphates vs citric.acid 0.31
sulphates vs chlorides 0.37
alcohol vs density -0.496
citric.acid vs volatile.acidity -0.552
total.sulfur.dioxide vs free.sulfur.dioxide 0.66
density vs citric.acid 0.36
density vs residual.sugar 0.35
Multivariate Analysis
Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
Looking the graphs above, I see the data overlap each other from quality 3 to quality 8. There are not much different between quality points.
Were there any interesting or surprising interactions between features?
One graph that is surprising to me is chorides vs sulphates. The quality 8 is the only one different from the others. The sulphates stay in range 0.5 to 1.0 for all qualities; However, chorides stays less than 0.1 for quality 8 only.
Final Plots and Summary
Plot One
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Description One
The plots above show that alcohol with quality 7 and 8 tend to higher than lower quality wine.
Plot Two
Description Two
Looking the plots avobe, I see that when the quality get higher: sulphates,alcohol tend to get higher and volatile.acidity tend to get lower.
Plot Three
Description Three
Looking at the plots, the sulphates stay mostly in range 0.5 to 1.0 for all qualities; However, chorides stays less than 0.1 for quality 8 only.
Reflection
Here are some characteristics for high quality wine: - Chlorides is below 0.1. Chlorides is the amount of salt in the wine. It means higher quality wine has less salty than lower quality wine. - When the quality get higher, sulphates tends to get higher. Sulphates is a wine additive which can contribute to sulfur dioxide gas (S02) levels, which acts as an antimicrobial and antioxidant. It can keep the consumers younger. - When the quality get higher, alcohol tends to get higher. It means stronger wines most likely have better quality. - When the quality get higher, citric.acid tends to get higher. Citric acid can add ‘freshness’ and flavor to wines. It can make high quality wine tastes better.
When the quality get higher, volatile.acidity tends to get lower. Volatile acidity is the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste. It means higher quality wine has pleasant taste than lower quality wine because volatile.acidity level is lower.
- When the quality get higher, density tends to get lower.
When the quality get higher, pH tends to get lower. pH describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic). Most wines are between 3-4 on the pH scale. When pH get lower, the acidic levels in wine increase.